Artificial Intelligence, Data and Competition – ČORBA – June 2024 OECD discussion

This presentation by Juraj Čorba, Chair of OECD Working Party on Artificial Intelligence Governance (AIGO), was made during the discussion “Artificial Intelligence, Data and Competition” held at the 143rd meeting of the OECD Competition Committee on 12 June 2024. More papers and presentations on the topic can be found at oe.cd/aicomp.

This presentation was uploaded with the author’s consent.

1. Governance of artificial
intelligence and the interplay
with competition policy
OECD Competition Committee
12 June 2024
Juraj Čorba
Chair, OECD Working Party on
Artificial Intelligence Governance
(AIGO)

2. 1. Background: What is AI?

3. Avariety of systems and policy implications
Myriad of applications of AI systems

4. What is Artificial Intelligence?
In November 2023, the OECD Council adopted an updated definition of an “AI system”:
An AI system is:
“A machine-based system that,
for explicit or implicit objectives,
infers, from the input it receives,
how to generate outputs such as predictions,
content, recommendations, or decisions
that can influence physical or virtual
environments.
Different AI systems vary in their levels of
autonomyand adaptivenessafter deployment.”
BUILDING AI SYSTEM
USING AI SYSTEM

5. 2. The OECD AI Principles

6. Respect for the rule of law,
human rights and democratic values,
including fairness and privacy
Transparency and Explainability
Robustness, Security, and Safety
Accountability
Inclusive growth, sustainable
development and well-being
5 values- based
principles for trustworthy,
human- centric AI
5 recommendations
for national policies, for AI
ecosystems to benefit societies
Investing in AI research and developmentFostering an inclusive AI-enabling
ecosystem
Shaping an enabling interoperable governance and policy environment for AI
Building human capacity and preparing for labour market transformation
International co- operation for trustworthy AI
The Revised OECD AI Principles

7. Shaping an enabling interoperable governance
and policy environment for AI (Principle 2.3)
[…]
Governmentsshould review and adapt, as
appropriate, their policy and regulatory
frameworks and assessment mechanisms as they
apply to AI systems toencourageinnovation and
competition for trustworthy AI.

8. 3. Key parts of the value chain: what is
required to successfully develop and
deploy AI models?

9. The AI system lifecycle
AI actors
those who play an active role in the AI system lifecycle, including organisations and individuals that
deploy or operate AI.

10. AI system
lifecycle
Human
resources
provider
Government
agency
Marketing
agency
Sales
department
Application
developer
Law firm
Data owner Data labeller
Investor
Dataset
curator
Chip
manufacturer
Illustrative example of actors
involved in the development
and use of AI
Suppliers of AI knowledge
& resources
Actors actively
involved in the
design,
development,
deployment,
and operation
of AI systems
Users of the AI system
Understanding the AI value chain

11. Investment
Key inputs to develop AI – “AI enablers”
Research
(Programming) skills /
skilled labour

12. Research

13. AI skills migration and penetration by country
(OECD members, 2022)
(average 2015-2022)

14. VC investments in AI
*generative adversarial network, generative AI, text generation, image generation, audio generation and generative model.
Investments in generative AI* (cumulative)Investments in AI (cumulative)

15. VC investments in data start-ups
(cumulative)

16. VC investments in AI compute start-ups
(cumulative)

17. Computational complexity and training costs
are rising
Average number of parameters of new AI models from Hugging FaceAverage training cost of new AI models from Hugging Face
Source: OECD.AI (2024), visualisations powered by JSU using data from Hugging Face, accessed on 22/4/2024

18. New models keep being developed
Source: OECD.AI (2024), visualisations powered by JSU using data from Hugging Face, accessed on 22/4/2024

19. Next steps
•More nuanced understanding of market
dynamics and AI actors is needed
•Monitoring (market) developments in such AI
enablers will be key to a comprehensive
understanding of competition in AI
•Cross-disciplinary exchanges can foster more
targeted policy responses

20. FOLLOW US:
@OECD-AI